(2024) 22:321
Valdes et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03543-4

BMC Medicine

RESEARCH

Open Access

Vitamin A carotenoids, but not retinoids,
mediate the impact of a healthy diet on gut
microbial diversity
Ana M. Valdes1,2,3*†, Panayiotis Louca3,4†, Alessia Visconti3,5, Francesco Asnicar6, Kate Bermingham7,8,
Ana Nogal3,9, Kari Wong10, Gregory A. Michelotti10, Jonathan Wolf8, Nicola Segata6, Tim D. Spector3,8,
Sarah E. Berry7,8, Mario Falchi3 and Cristina Menni3*

Abstract
Background Vitamin A is essential for physiological processes like vision and immunity. Vitamin A’s effect on gut
microbiome composition, which affects absorption and metabolism of other vitamins, is still unknown. Here we
examined the relationship between gut metagenome composition and six vitamin A-related metabolites (tworetinoid: -retinol, 4 oxoretinoic acid (oxoRA) and four carotenoid metabolites, including beta-cryptoxanthin and threecarotene diols).
Methods We included 1053 individuals from the TwinsUK cohort with vitamin A-related metabolites measured
in serum and faeces, diet history, and gut microbiome composition assessed by shotgun metagenome sequencing.
Results were replicated in 327 women from the ZOE PREDICT-1 study.
Results Five vitamin A-related serum metabolites were positively correlated with microbiome alpha diversity (r = 0.15
to r = 0.20, p < 4 × ­10−6). Carotenoid compounds were positively correlated with the short-chain fatty-acid-producing
bacteria Faecalibacterium prausnitzii and Coprococcus eutactus. Retinol was not associated with any microbial species. We found that gut microbiome composition could predict circulating levels of carotenoids and oxoretinoic acidwith AUCs ranging from 0.66 to 0.74 using random forest models, but not retinol (AUC = 0.52).
The healthy eating index (HEI) was strongly associated with gut microbiome diversity and with all carotenoid compounds, but not retinoids. We investigated the mediating role of carotenoid compounds on the effect of a healthy
diet (HEI) on gut microbiome diversity, finding that carotenoids significantly mediated between 18 and 25%of the effect of HEI on gut microbiome alpha diversity.
Conclusions Our results show strong links between circulating carotene compounds and gut microbiome composition and potential links to a healthy diet pattern.
Keywords Vitamin A metabolites, Carotenoids, Retinoids, Gut microbiome composition†

Ana M. Valdes and Panayiotis Louca contributed equally to this work.

*Correspondence:
Ana M. Valdes
ana.valdes@nottingham.ac.uk
Cristina Menni
cristina.menni@kcl.ac.uk
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Valdes et al. BMC Medicine(2024) 22:321

Background
Vitamins are micronutrients with antioxidant and neuroprotective properties. In addition to these functions,some vitamins have been linked to changes in the composition and diversity of the gut microbiome [1]. The
human body requires 13 vitamins for proper physiological function, which can be divided into two categories,
fat-soluble (vitamins A, D, E, and K), and water-soluble
vitamins (8 B vitamins, and vitamin C).
Fat-soluble vitamins, particularly vitamins A, and D,
have been studied for their effects on the immune system
when absorbed in the gastrointestinal tract and secreted
into the bloodstream [2]. A recent cross-sectional studyof 567 older people found that those with higher levelsof vitamin D had a greater abundance of butyrate-producing bacteria in the gut [3]. However, the relationship between vitamin A levels and the gut microbiome
remains under-researched.
Vitamin A is an essential macronutrient for maintaining the integrity of the gut epithelial barrier [4, 5]. It supports the production and maintenance of mucus, tightjunction proteins [6], and antimicrobial peptides, all ofwhich protect the gut lining from pathogenic invasion
and maintain a stable environment for the microbiota. A
healthy gut barrier prevents dysbiosis (microbial imbalance) and supports a diverse microbial ecosystem [7].
Moreover, vitamin A has anti-inflammatory properties[8], which can help mitigate chronic inflammation in thegut [9]. By reducing inflammation and supporting the
gut epithelial barrier, vitamin A fosters a more stable anddiverse microbial environment increasing the abundanceof beneficial commensals and decreasing the abundanceof less favourable ones.
Vitamin A is derived from both retinyl acids found inanimal-derived foods, or through a series of enzymatic
reactions from carotenes and carotenoids in plant-based
foods. Although the term vitamin A is mostly associatedwith retinol, and retinol is, in fact, the predominant formof retinoids in the human body, the main biologically
active molecules are the oxidised derivates 11-cis-retinal
and all-trans-retinoid acid (ATRA) [10]. ATRA acts as
the active form, binding to retinoic acid receptors.
More than 80% of vitamin A in the liver is stored inhepatic stellate cells [11]. In hepatocytes, retinyl esters
are hydrolysed by retinyl ester hydrolase to generate retinol, which subsequently binds to retinol-binding protein(RBP), before being released into circulation, where it is
up-taken by systemic cells via membrane receptors, suchas STRA6 [12]. The process of mobilising retinol is tightly
regulated by variables that govern the rates of synthesis
and secretion of RBP [10].
Since, the human body cannot produce vitamin A,
it must be obtained from the diet, either as preformed

Page 2 of 11

vitamin A, found in foods of an animal origin, or as provitamin A carotenoids, found in several fruit and vegetables [10]. Milk and dairy products, as well as meat and its
products, are the largest contributors of preformed vitamin A to the human diet, followed by eggs, egg products,
and fish [10, 13].
Carotenoids can eventually be metabolised to retinol[14]. Retinoic acid has been shown to be an important
determinant of intestinal immunity and permeability [15].
Importantly, absorption of dietary vitamin A depends onthe fat-solubilising properties of bile acids [16], which arein turn modulated by gut microbiome composition, andbacterial conjugation of secondary bile acids [17].
Given that diets rich in carotenoids are likely to support a healthy gut microbiome [18] and the multiple beneficial effects of vitamin A on immune function and gut
barrier integrity [18, 19], we hypothesised a positive relationship between alpha diversity and vitamin A.
The purpose of this study is to investigate whether
there is a connection between the composition and alphadiversity of the gut microbiome and the presence of vitamin A-related metabolites, specifically retinoids andcarotenoids, in the bloodstream and stool, using machinelearning methods. We further quantify the extent towhich bile acids influence the levels of retinoids andcarotenoids. Finally, we investigate the role that vitamin
A metabolites play in the favourable association between
the healthy eating index (HEI) [20] and the compositionof the gut microbiome.

Methods
Discovery cohort

Study participants were individuals enrolled in the TwinsUK registry, a national register of adult twins recruited
as volunteers without selecting for any particular diseaseor trait [21]. Twins provided informed written consent,
and the study was approved by St. Thomas’ Hospital
Research Ethics Committee (REC Ref: EC04/015). Here
we included 1053 individuals with concurrent vitamin A
metabolites, bile acids measured by mass spectrometry,and gut microbiome profiled by shotgun metagenome. A
subset of the included individuals also had available dataon the quality of their habitual diet, as measured by the
HEI [20].
Replication cohort

The replication cohort consisted of an independent sample of 327 females from the UK-based ZOE PREDICT-1
study [22]—in a post hoc analysis, with serum and stool
vitamin A metabolites measured by Metabolon Inc. aswell as whole-shotgun metagenomic data available, andwho completed a food frequency questionnaire (FFQ).
Ethical approval for ZOE PREDICT-1 was obtained from

Valdes et al. BMC Medicine(2024) 22:321

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St. Thomas Hospital research ethics committees. All
individuals provided informed written consent (IRAS
236407) and the trial was registered on ClinicalTrials.gov(registration number: NCT03479866).

was performed as previously described [28, 29]. To
reduce noise caused by species with low prevalence andimprove power to detect statistically significant species,
we removed species with a prevalence < 20%.

Vitamin A metabolomics profiling

Statistical analysis

Vitamin A concentrations were measured from stool andserum samples by Metabolon Inc. (Durham, USA) using
an untargeted LC–MS platform, as previously described[23, 24]. Six vitamin A-related metabolites were detected
in serum, of which two retinols and four carotenoids,
while five vitamin A-related metabolites were detectedin stool (one retinol and four carotenoids). To remove
batch variability from vitamin A profiling, for each vitamin A-related metabolite, the values in the experimental
samples were divided by the median of those samples ineach instrument batch, giving each batch and thus eachvitamin A metabolite a median of one. Vitamin A metabolites with more than 80% missing measurements were
excluded, and those with missingness between 20 and
80% were dichotomised. Vitamin A metabolites present
in over 80% of the sample were batch normalised andinverse normalised. After cleaning, 6 vitamin A metabolites in serum (2 retinols and 4 carotenoids) and 5 instool (1 retinol and 4 carotenoids) remained. Carotenoid
metabolites include beta-cryptoxanthin, and carotene
diol (1–3). Retinol metabolites include 4-oxoretinol acid
(oxoRA) and retinol.

Statistical analyses were performed using Stata version
18 and R version 4.3.1 [30].
For alpha diversity, we constructed several metrics toquantify diversity of the gut microbiome within individual participants. The Shannon diversity index also called
Shannon Entropy [31] uses Claude Shannon’s formula for
entropy to estimate species diversity [22] and the Simpson diversity index, which gives more weight to commonor dominant species, were calculated using the ‘diversity’function in the ‘vegan’ R package [32]. Observed richness, which is simply the count of species observed, wascalculated using the ‘specnumber’ function. To accountfor the twin nature of our data, linear mixed models
adjusting for age, sex, body mass index (BMI) as fixed
effect, and family structure, as random effect, were used
independently in both TwinsUK and ZOE PREDICT-1 toinvestigate the univariate associations between vitamin
A-related metabolites and gut microbiome characteristics. Specifically, we investigated associations with severalalpha diversity metrics and at the taxonomic level withinverse normalised species abundances. P-values werecorrected for multiple testing using Bonferroni correction, and associations with adjusted P-value < 0.05 wereconsidered as significant. Results were combined using
fixed effect meta-analyses. To assess between participant
differences (beta diversity) and vitamin A-related metabolite levels, we constructed a Bray–Curtis dissimilarity
matrix from the gut microbial community data, and we
performed a PERMANOVA (1000 simulations) using the‘adonis’ function (‘vegan’ package [32]) to determine significance, adjusting for age, sex, and BMI.
To quantify how much of the vitamin A-related metabolite levels could be predicted using gut metagenome
data, we used random forest (RF) models from the randomForest package in R [33] (version 4.7–1.1). Models
were conducted in TwinsUK and ZOE-PREDICT-1 data
independently. We split the dataset into a training set(80%) and a test set (20%), which was held out to test performance. In the training data, predictors with zero ornear-zero variance were excluded using the ‘nearZeroVar’
function (caret R package [34]). Hyperparameters, mtry
(number of variables randomly sampled as candidates foreach split) and min.node.size (minimum size of terminal
nodes in each tree) were tuned using fivefold adaptive
resampling (caret package in R [34]) with 3 repeats. The
optimal number of features for each model was decided
by fivefold recursive feature elimination (‘rfcv’ function

Bile acid metabolomics profiling

Circulating primary bile acids, including cholic acid(CA), chenodeoxycholic acid (CDCA), taurocholic acid
(TCA), glycocholic acid (GCA), taurochenodeoxycholic acid (TCDCA), and glycochenodeoxycholic acid
(GCDCA) were measured from the same serum samples
by Metabolon Inc. (Durham, USA) using an untargeted
LC–MS platform, as described above.
Dietary information

A validated 131-item semi-quantitative food frequency
questionnaire (FFQ) established for the European Prospective Investigations into Cancer and Nutrition(EPIC)-Norfolk study [25] was used to estimate habitual
dietary information. From FFQs, food item, macro- andmicronutrient intakes were determined using FETA software [25, 26], we then calculated indexes to represent thewhole dietary pattern, including HEI, which characterises
intakes of foods and nutrients and is understood to be
associated with various chronic diseases [27].
Microbiome sequencing and profiling

Deep shotgun metagenomic sequencing in stool samples
from TwinsUK and ZOE PREDICT-1 and its profilingValdes et al. BMC Medicine(2024) 22:321

from the randomForest package [33]). We iteratively
removed features by assessing the model’s performanceagainst the cross-validated error rate to determine theoptimal subset of features (smallest number of featureswith smallest error rate). The model was retrained on the
training data and the predictive performance tested using
the test set. The performance of each model was assessedusing the area under the receiver operating characteristic (AUC), which measures the model’s capacity to discriminate different classes and Spearman’s correlations
between the model’s predicted class and the true class toquantify the model’s accuracy and reliability.
Linear mixed models were also employed to investigate
the associations between vitamin A-related metabolites
and (i) serum levels of bile acids and (ii) HEI, adjusting for age, sex, BMI, and family relatedness as random
effects.
To investigate whether vitamin A-related metabolites
mediate the relationship between a healthy diet and gut
microbiome diversity, we conducted causal mediation
analyses using the Baron and Kenny approach [35]. We
first tested the three mediation assumptions and we thenperformed causal mediation analysis using the ‘mediate’ function in the R package ‘mediation’ (version 4.5.0)[36]. Each analysis was performed independently foreach vitamin A metabolite and adjusted for age, sex, and
BMI. A significant mediatory effect was determined byPage 4 of 11

the significance (p < 0.05) and magnitude of the indirect
effect. We further computed the variance accounted for(VAF) as the ratio of the indirect-to-total effect, which
describes the proportion of the variance explained by
the mediation process, e.g. the proportion of the effectof a healthy diet on gut microbiome that goes throughthe vitamin A metabolite. We used Vanderweele’s sensitivity analysis [37, 38] to evaluate the robustness of theestimated mediation effects to unmeasured confounding.
This involves assessing the potential impact of unmeasured confounders on both the mediator-outcome andexposure-mediator relationships.

Results
A flowchart of the study design is presented in Fig. 1.
The descriptive characteristics of the study populations
are presented in Table 1. We included 1053 individuals
from the TwinsUK cohort [21], and 327 female individuals from the ZOE PREDICT-1 study [21, 22], with vitamin A metabolites measured in serum (n = 6) and stool(n = 5) by Metabolon Inc. [24], and gut microbiome composition assessed by whole-shotgun metagenomics. The
average (standard deviation, SD) age in TwinsUK was
57.72 (15.20), and in ZOE PREDICT-1 53.80 (7.00) years,
while average body mass index (BMI) was 26.18 (5.15)
and 26.24 (5.62) kg/m2, respectively.

Fig. 1 Schematic representation of the study. The figure describes the links between dietary intake and the studied vitamin A-related metabolites,
and highlights the research questions we are addressing, the data, and the analytical workflow used

Valdes et al. BMC Medicine(2024) 22:321

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Table 1 Descriptive characteristics of the study populations
PhenotypeTwinsUK

ZOE-PREDICT 1

N

1053

327

Females, n (%)

884 (83.95%)

327 (100%)

Age, years (SD)57.72 (15.20)

53.80 (7.00)

BMI, kg/m2

26.18 (5.15)

26.24 (5.62)

HEI (SD)

59.27 (9.93)

65.42 (7.02)

Shannon diversity index(SD)

3.91 (0.54)

4.23 (0.38)

BMI body mass index, HEI healthy eating index

Vitamin A metabolites and gut microbiome association

All vitamin A-related serum metabolites except retinol were positively correlated with the Shannon diversity index after adjusting for age, sex, BMI, and family
relatedness in the TwinsUK cohort (Fig. 2, panels A
and B). Further adjusting for history of cardiovasculardiseases (including cerebrovascular disease, heart failure, ischemic heart disease, coronary artery disease,and atrial fibrillation), type 2 diabetes, chronic obstructive pulmonary disease, allergy, diet (including healthyeating index, fibre intake, vegetable intake, and energy

Fig. 2 Associations between vitamin A metabolites from serum and stool and the Shannon diversity index (A and B) and the healthy eating
index (C and D). Data shown. Circles show effect sizes in the TwinsUK and ZOE PREDICT 1 cohorts, diamonds are summary effect sizes derived
from fixed effects meta-analysis (MA) combining both cohorts. Whiskers represent the 95% confidence intervals. Statistical significance is indicatedby asterisks as follows: * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001

Valdes et al. BMC Medicine(2024) 22:321

intake), antibiotics use, physical exercise, vitamin supplementation, and sequencing depth, did not change theresults (Additional File 1:TableS1). Consistent results
were observed in ZOE-PREDICT 1 (Fig. 2, panels A
and B) and when investigating the correlation with the
Simpson index and with the number of observed species (Additional File 1:TableS2). Moreover, we detecteda significant relationship between vitamin A metabolites
and beta-diversity (as determined by Bray–Curtis indices), after adjusting for age, sex, and BMI (Additional File
1:TableS3).
We then investigated, in TwinsUK, the univariate association between vitamin A-related metabolites and gut
microbial species with a prevalence > 20%, identifying
96 significant associations (Bonferroni < 0.05), involving 49 unique bacterial species (Fig. 3A, Additional File
1:TableS4). Similar but weaker associations were seen
with stool metabolite levels (Fig. 3A). Among the associated species, we identified Faecalibacterium prausnitzii
and Coprococcus eutactus to be positively correlated withall carotenoid metabolites, whereas negative correlations
included species such as Ruminococcus torques and R.
gnavus and Eggerthella lenta, Tyzzerella nexilis, and Flavonifractor plautii. No association was found between
individual taxa and serum or stool levels of retinol.
Next, we used random forest models to measure theability of microbial species abundances to predict circulating vitamin A-related metabolite levels and generate a quantitative estimate of the extent to which gut
microbiome composition is linked to vitamin A metabolite levels underscoring the importance of gut health inoverall nutritional and anti-inflammatory status. The
model performance was evaluated using the area under
the receiver operating characteristic curve (AUC) andcalculating Spearman’s correlations between predicted
and observed values (Rho). In TwinsUK, on average, thegut microbiome was able to predict circulating levels ofcarotenoid metabolites across the 5-folds (Fig. 3B), withcarotene diol (2) presenting the strongest association(AUC [95%CI] = 0.74 [0.67; 0.81], rho [95%CI] = 0.31
[0.15, 0.45]).
We performed a sensitivity analysis to explore whether
our results were dependent on the transformation used

Page 6 of 11

on the gut microbiome (inverse normalisation). We
transformed the data using the centred log-ratio (CLR)transformation and results were consistent (e.g. carotenediol (2) AUC [95% CI] = 0.68 [0.6; 0.76], rho [95%CI] = 0.25 [0.1; 0.4]).
We replicated the predictive models in the ZOEPREDICT-1 cohort. Consistent with the observations in
TwinsUK, the gut microbiome was able to predict circulating levels of carotenoid metabolites with the largest
AUC for carotene diol (2) (0.73 [0.60, 0.86]) (Fig. 3B).
Vitamin A metabolites and bile acid levels

In TwinsUK, we quantified the effect of circulating primary bile acids on carotenoid and retinoid circulatinglevels to test whether fat absorption, which is modulated
by primary BAs [4], influenced serum vitamin A levels.
We tested their correlation with CA and CDCA, and theirconjugated bile salts TCA, GCA, TCDCA, and GCDCA,
which have been suggested in the literature to influence
vitamin A absorption and metabolism [16], and which
are known to have strong links to gut microbiome composition and function [39, 40]. We found three vitamin
A-related metabolites nominally associated with bile acidlevels: carotene diol (2) was negatively correlated withboth GA (beta [95% CI] =  − 0.08 [− 0.14, − 0.07], p = 0.01)
and GCDCA (beta [95% CI] =  − 0.06 [− 0.12, − 0.004],
p = 0.03), carotene diol (1) was positively associate withCA (beta [95% CI] = 0.07[0.001,0.15], p = 0.05], while
oxoRA was positively associated with CA (beta [95%CI] = 0.07, [0.001, 0.16], p = 0.045, and 0.12 [0.05, 0.12],
p = 5.43 × ­10−4, respectively, Additional File 1:Fig. 1).
After adjusting for multiple testing (Bonferroni p-value= 0.05/30 = 1.6 × ­10−3), only oxoRA was associated withCA levels. None of the carotenoids nor retinol were correlated with primary bile acids after adjustment for multiple testing.
Vitamin A metabolites and diet quality association

We further investigated the association between serumand stool vitamin A-related metabolites, and the qualityof habitual diet, as measured by the HEI in a subset of
664 TwinsUK individuals with FFQs available. We foundstrong positive associations between serum carotenoids(See figure on next page.)
Fig. 3 A Heatmap representing correlation of vitamin A-related metabolites from serum and gut bacterial species. Each cell of the matrix contains
the regression coefficient between one vitamin A-related metabolite and a bacterial species. The table is colour-coded by correlation according
to the table legend (red for positive and blue for negative correlations). * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. B Prediction of the gut
microbiota in vitamin A-related metabolites estimated by random forestregressors (using Spearman’s correlations) and classifiers (using AUC
values) in TwinsUKand ZOE PREDICT-1 participants. Boxplots represent the mean AUC and the 95% confidence intervals across fivefold for TwinsUK
and ZOE PREDICT-1. Dark blue and light blue circles indicate the mean of Spearman’s correlations between the real value of each vitamin A-related
metabolite and the value predicted by the models across fivefold in TwinsUK and ZOE PREDICT-1

Valdes et al. BMC Medicine(2024) 22:321

Fig. 3 (See legend on previous page.)

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Valdes et al. BMC Medicine(2024) 22:321

and HEI after adjusting for covariates, while no effects
were observed for retinols or for stool vitamin A metabolite abundances (Fig. 2C). Results were replicated in PREDICT 1 (Fig. 2D).
Consistent with the literature [41], we found a positive association between the HEI and the Shannon diversity index (beta [95% CI] = 0.10 [0.02, 0.19], p = 0.01)
after adjusting for age, sex, BMI, and family relatedness.
Importantly, when adjusting for each of the carotenoids
in turn, the effects were attenuated (Additional File
1:TableS5). We, therefore, performed a formal mediation analysis adjusting for age, BMI, and sex to determine whether the serum carotenoid metabolites mediate
the association between HEI and the Shannon diversity
index. We only focused on carotenoids mediating theeffects of healthy diet on the microbiome and not viceversa as experimental evidence has shown that in anaerobic conditions (as is the case in the human colon) thatgut microbes cannot produce carotenoids [42, 43]). Allfour carotenoid metabolites met the criteria for a mediator, as laid out by Baron and Kenny [35]. Following a formal mediation analysis, all four carotenoids significantly
mediated the effect of a healthy diet (HEI) on gut microbial diversity (Fig. 4). The variance accounted for ranged
from 19.8% for beta-cryptoxanthin to 39.9% for carotene
diol (1).
However, our findings surrounding carotene diol
(1) and (2) could be sensitive to unmeasured residual

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confounding (e-values = 1.32 [LowerBound = 1.0] [37, 38](Additional File 1:TableS6). On the other hand, for carotene diol (3) and beta-cryptoxanthin, our results appear
robust (e-values = 1.35 [LowerBound = 1.04].

Discussion
In this large-scale study investigating the links betweenstool and serum vitamin A-related metabolites and thegut microbiome, we report that carotenoids are strongly
correlated with gut microbiome composition, while retinoids are only weakly associated with gut microbes.
In addition to strong positive associations with alphadiversity, we also observed significant positive correlations between circulating levels of carotenoid compounds
and the abundance of short-chain fatty acid (SCFA) producing gut microbes, such as Faecalibacterium prausnitzii and Coprococcus eutactus [44–46]. Carotenoids are
known for their antioxidant properties [47], which may
contribute to a higher abundance of beneficial bacterial
species. Similarly, we found that carotenoids were negatively correlated with species previously linked to inflammation (Ruminococcus torques, Ruminococcus gnavus)[48], or unfavourable cardiometabolic outcomes [29] aswell as colorectal cancer risk and progression (Eggerthella
lenta, Tyzzerella nexilis, Flavonifractor plautii) [49–51].
Consistently, the levels of these compounds in serum,
though much less in stool, were partially predicted by
the gut microbiome composition using random forest

Fig. 4 Mediation analysis diagram. A healthy diet, represented by the healthy eating index, was modelled as the exposure. Alpha diversityof the gut microbiome, measured by the Shannon diversity index, was modelled as the outcome. Each vitamin A metabolite was modelled
as a mediator. Summary statistics from the mediation analysis are depicted as beta (95% confidence interval). Abbreviations: HEI, healthy eating
index; CI, confidence interval

Valdes et al. BMC Medicine(2024) 22:321

models and, importantly, these results were replicatedin an independent cohort. The lack of prediction of thegut microbiome on stool carotenoid and retinoid levels
may be reflecting that absorption of these substances
takes place mostly in the proximal intestine (jejunum andduodenum) [52] and hence it is likely that the systemic
effects of carotenoids and retinoids influence the gut
microbiome and not gut microbes affecting absorption ofthese fat-soluble molecules.
We also report that, although all these compounds are
fat soluble, levels of primary bile acids (which are key inthe absorption of fat substances and have been implicated in the absorption and metabolism of vitamin A
and its metabolites [16]) are not correlated with circulating levels of carotenoid compounds or of retinol. Strong
links between bile acid metabolism and gut microbiome
composition have been reported [17, 40], but the associations reported here with gut microbiome diversity
are unlikely to be caused by the effects of the gut microbiome on absorption in the upper GI tract, suggestingthat this mechanism may play only a small role in determining levels of circulating carotenoids and retinoids
within the ranges seen in a healthy population. Indeed,
recent experimental data has shown the inability of gut
microbes to synthesise carotenoids in anaerobic conditions comparable to the human gut [43], hence our data
are consistent with dietary carotenoids influencing the
gut microbiome composition and not vice versa.
Finally, we showed that, to some extent, the effect ofeating a healthy diet (as measured by adherence to HEI)
on gut microbiome alpha diversity is mediated by levelsof carotenoid compounds. The proportion of the effectranged from 19.8% for beta-cryptoxanthin to 39.9% forcarotene diol (1). No significant effect was seen for retinoid compounds. These results could have potential
implications for personalised nutrition, and the development of dietary interventions to promote a healthy
gut microbiome [53]. Indeed, these data highlight strong
links between fat-soluble carotenoids and beneficial gut
microbes, suggesting the potential of modulating someof these species by carotenoid intake. Further research inthis area may contribute to the development of targeted
dietary recommendations for individuals aiming to optimise their gut microbial composition and promote overall health.
Our study has many strengths. First, we used a wellvalidated large population-based cohort for discovery
and validated our findings in an independent cohort.
Both cohorts had the same omics profiling and were processed using the same quality control and analysis pipelines. Second, we used whole-shotgun metagenomics
data, which provides considerably more depth than characterisation via 16S rRNA gene amplicon data. Third, wePage 9 of 11

measured vitamin A metabolites as well as several primary and secondary bile acids in both serum and stool
using one of the best-characterised targeted commercially available metabolomic panels.
We also note some limitations. First, our study sample is predominantly female, and our replication cohort
included females only. Second, vitamin A metabolite
levels the metabolomics panel used provides relative values, and not absolute concentrations of the metabolites
in serum or stool. This limitation also affects the clinicalinterpretability of our results, where absolute quantification is necessary for comparing results to clinical thresholds used to diagnose/manage vitamin A deficiency
or toxicity. Nevertheless, our results indicate relativechanges and associations facilitating deeper exploration
within future studies and subsequent improved clinical relevance. Third, dietary intake was measured using
a food frequency questionnaire, which has inherent
biases, including response bias and potential misclassification. Fourth, our mediation analysis may not truly
identify a causal relationship, due to a lack of a temporal relationship between measurement of our exposure,
mediator, and outcome, and we are unable to exclude thepossibility of residual unmeasured confounders, whichhighlights the need for further research into the interactions between vitamin A-related metabolites and thegut microbiome, and to understand what, if any, such
unmeasured confounders might be. Nevertheless, we
successfully replicated the results in both TwinsUK and
ZOE Predict-1. Finally, we are unable to infer causality
between carotenoids levels, gut microbiome composition, and HEI.

Conclusions
In summary, this is, to the best of our knowledge, the firstlarge-scale study investigating the links between vitamin
A metabolites and the gut microbiome using wholegenome shotgun metagenomic sequencing. Here, weshow that carotenoids but not retinoids are strongly correlated with higher alpha diversity and with higher relative abundance of beneficial bacteria, and that vitamin A
metabolites mediate between 20 and 39% of the effects ofadherence to a healthy diet on microbiome diversity.
Abbreviations
ATRA​	All-trans-retinoid acid
BMI	Body mass index
CA	Cholic acid
CDCA	Chenodeoxycholic acid
FFQ	Food frequency questionnaire
GCA​	Glycocholic acid
GCDCA	Glycochenodeoxycholic acid
HEI	Healthy eating index
oxoRA	4-Oxoretinol acid
RAR​	Retinoic acid receptors
RBP	Retinol-binding protein

Valdes et al. BMC Medicine(2024) 22:321

TCA​	Taurocholic acid
TCDCA	Taurochenodeoxycholic acid

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03543-4.
Supplementary Material 1: Table S1. Association between Shannon
Diversity and vitamin A related metabolites adjusting for age, sex, BMI, history of cardiovascular diseases (i.e. cerebrovascular disease, heart failure,ischemic heart disease, coronary artery disease and atrial fibrillation), type2 diabetes, chronic obstructive pulmonary disease, allergy, diet (i.e. healthyeating index, fibre intake, vegetable intake and energy intake), antibiotics
use, physical exercise, vitamin supplementation, sequencing depth andfamily relatedness in TwinsUK. In this analysis, missing dietary values were
imputed to the mean. Table S2. Association between Alpha Diversity indices and vitamin A related metabolites adjusting for age, sex, BMI and family relatedness in TwinsUK and PREDICT-1. Beta (SE) and P value for fixed
effect meta-analysis are reported. Table S3. Beta-diversity estimates for gut
microbiome composition in relation to serum and stool vitamin A-related
metabolites using PERMANOVA (Bray-Curtis dissimilarity). Results of the
PERMANOVA analysis as implemented in ADONIS, examining the association between gut microbiome beta-diversity using Bray-Curtis dissimilarity
and serum and stool vitamin A-related metabolites. The table includes theR-squared (R²) values, and p-values for each metabolite unadjusted andadjusted for covariates (age, sex, BMI and family relatedness). The R² value
represents the proportion of total variation in gut microbiome composition explained by each metabolite. P-values are derived from 1000
permutations and indicate the statistical significance of the observed
associations. Table S4. Association between circulating vitamin A-related
metabolites and bacterial species adjusting for age, sex, BMI, family relatedness and multiple testing in TwinsUK. Table S5. Association between
Shannon and HEI, adjusted for age, sex, BMI, and family relatedness andthe below carotenoids. Table S6. Sensitivity Analysis for Unmeasured
Confounding using the E-value method. Figure S1. Association between
vitamin A related metabolites and circulating primary bile acids.
Acknowledgements
We express our sincere thanks to all the participants of the TwinsUK and ZOEPREDICT-1, including those that devoted their time to the dietary intervention trial. We thank the staff working on each of the cohorts, and Zoe Global
Limited, for their tireless work in contributing to the running of the study anddata collection.
Authors’ contributions
Conceived and designed the experiments: AMV and CM. Contributed
reagents/materials/analysis tools: AV, FA, KB, AN, KW, GAM, JW, NS, TDS, SEB,
and MF. Data curation: AV, FA, PL, and CM. Formal analysis: PL and CM. Wrotethe manuscript: AMV and CM. Revisedthe manuscript: all. All authors read andapproved the final manuscript.
Funding
This research was funded in whole, or in part, by the Wellcome Trust(WT212904/Z/18/Z). For the purpose of open access, the authors have applied
a CC BY public copyright to any Author Accepted Manuscript version arisingfrom this submission. The Department of Twin Research receives support from
grants from the Wellcome Trust (212904/Z/18/Z) and the Medical ResearchCouncil (MRC)/British Heart Foundation (BHF) Ancestry and Biological Informative Markers for Stratification of Hypertension (AIM-HY; MR/M016560/1), European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd., the
NIHR Clinical Research Facility and Biomedical Research Centre (based at Guy’s
and St Thomas’ NHS Foundation Trust in partnership with King’s College London). CM is funded by the Chronic Disease Research Foundation (CDRF) andby the MRC Aim-Hy project grant, AMV is supported by the National Institutefor Health and Care Research Nottingham Biomedical Research Centre. PL isfunded by the CDRF (CDRF–15/2018). Support for this work was also provided
by UKRI/MRC grants MR/W026813/1 and MR/Y010175/1 to AMV and CM.

Page 10 of 11

Availability of data and materials
The data used in this study are held by the Department of Twin Research atKing’s College London. The data can be released to bona fide researchers
using our normal procedures overseen by the Wellcome Trust and its guidelines as part of our core funding (https://​twins​uk.​ac.​uk/​resou​rces-​for-​resea​rchers/​access-​our-​data/). The gut microbiome data is available on EBI (https://​www.​ebi.​ac.​uk/) under accession number PRJEB39223 (ZOE- PREDICT-1) and
PRJEB32731 (TwinsUK).

Declarations
Ethics approval and consent to participate
All participants provided written informed consent. TwinsUK was approvedby the St Thomas’ Hospital Research Ethics Committee (REC Ref: EC04/015).
Ethical approval for ZOE PREDICT-1 was obtained from St. Thomas Hospitalresearch ethics committees and all individuals provided informed writtenconsent (IRAS 236407) and the trial was registered on ClinicalTrials.gov (registration number: NCT03479866).
Consent for publication
Not applicable.
Competing interests
TDS is a co-founder and shareholder of ZOE Ltd. AMV, SEB, KB, FA, and NS
are consultants to Zoe Ltd (‘Zoe’). JW is an employee of Zoe. KW and GAM
are employees of Metabolon Inc. Other authors declare that they have nocompeting interests.
Supplementary information.
Author details
1
Nottingham NIHR Biomedical Research Centre at the School of Medicine,
University of Nottingham, Nottingham NG5 1PB, UK. 2 Inflammation, Recovery
and Injury Sciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK. 3 Department of Twin Research & Genetic Epidemiology,
King’s College London, London SE1 7EH, UK. 4 Human Nutrition and Exercise
Research Centre, University of Newcastle, Newcastle Upon Tyne NE2 4HH, UK.
5
Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, 10124 Turin, Italy. 6 Department
CIBIO, University of Trento, Via Sommarive 9, 38123 Povo, Trento, Italy. 7 Department of Nutritional Sciences, King’s College London, 150 Stamford Street,
London SE1 9NH, UK. 8 Zoe Limited, 164 Westminster Bridge Rd, London SE1
7RW, UK. 9 Department of Epidemiology, Harvard T.H. Chan School of Public
Health, Boston, MA 02115, USA. 10 Metabolon Inc, Research Triangle Park, Morrisville, NC 27560, USA.
Received: 10 April 2024 Accepted: 28 July 2024

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